Solving the patient identity problem with universal identifiers

by Experian Health 5 min read March 2, 2022

Solving-the-patient-identity-problem-with-universal-identifiers-blog

Solving the patient identity problem and ensuring that each patient record is accurate and airtight is a top priority. Healthcare providers want to be 100% confident in answering “yes” to the following questions:

  • Is the patient who they say they are?
  • Is the right medication being administered to the right person?
  • Is the correct bill being sent to the patient’s current address?

By validating patient identities, providers can secure patient trust, deliver high-quality care, and avoid losing revenue to identity errors and fraud. Unfortunately, patient identity management is only becoming more complex. While telehealth and remote patient access are opening healthcare’s digital front door to meet changing consumer needs and expectations, a mountain of sensitive patient data is piling up.

This data is a gold mine for fraudsters who steal and sell patients’ personal information or use it to access services and prescriptions without paying. It’s distressing for patients and creates a major financial and administrative burden for healthcare staff.

A nationwide patient identification system may still be some way off. However, providers can optimize patient matching in their own health systems by working to reduce vulnerabilities and adopting cutting-edge interoperable patient matching technology.

Better patient matching means better patient care and protected profits

The human cost of incorrect, incomplete or outdated patient medical records is significant. Patients could be given the wrong medication or diagnostic procedures. Allergy information can be missed. Patient test results can be mislabeled or mixed up. In Experian Health’s State of Patient Access 2.0 survey, almost half of providers said inaccurate and incomplete patient data was an obstacle to proactive follow-up, which could cause gaps in care and avoidable complications – which are critical to value-based care compensation.

Duplicate and mismatched patient records also create massive inefficiencies that can threaten an organization’s financial health too. With telehealth claim lines climbing by 2817% between December 2019 and December 2020, reliably authenticating patient identities in both existing and new services will be critical to future financial performance.

Resolve, protect and enrich patient identities with universal identifiers

Having the right technology to resolve and secure a patient’s information when they log on to patient portals and telehealth systems is the first step. Automating patient enrollment with Experian Health’s PreciseID® ensures the patient is who they say they are. This solution utilizes best practices in identity-proofing, fraud management and device recognition.

But this system only works if the records being matched are accurate. A universal patient identifier provides a single, accurate, 360° view of each patient throughout their healthcare journey. An interoperable format allows systems to talk to each other and protects against duplicates, errors, inefficiencies and fraudulent activity.

Universal identifiers aren’t available nationwide yet, though there has been some encouraging movement. Congress is working to remove the ban on funding for such measures, while the Centers for Medicare and Medicaid Services are taking steps to promote data standardization. For health systems that want to maintain a golden record for each patient within the bounds of their own operations, Experian Health’s Universal Patient Identifier allows staff to connect, verify and protect patient information.

Choosing the right patient matching technology

Traditional matching technology relies on demographic data and uses deterministic or probabilistic methods to link records with identical identification information. However, relying on a single source of data means that previous errors are inherited by new versions of a patient’s record. Demographic data isn’t unique to individual patients, which can lead to mismatched records and create extra manual work to fix.

Experian Health uses referential matching technology to build a complete view of patients from reliable health, credit, and consumer data sources. The universal patient identifier connects disparate datasets and instantly updates the master index of patient records with new data points.

Referential matching can only ever be as good as the data that is being matched and Experian is a global leader in data accuracy, across numerous sources, and is continually updated.

Victoria Dames, VP of Product Management at Experian Health, says, “With Experian’s reference data, we’re able to create a longitudinal record of each individual and reconcile their data as they change names, addresses and see different providers. You need to know that it’s the same person, especially with the pandemic acting as a catalyst for digital technologies such as telehealth. It also helps organizations bring data together and ensure data integrity through mergers and acquisitions. Dealing with large volumes of data is a big hill to climb, but with the right technologies it can be that much faster.”

As telehealth and digital patient access services gain traction, solving the patient identity problem becomes increasingly urgent. Universal Identity Manager combines industry-leading consumer demographic information with the highest quality reference data and powerful unique patient identifiers to create a single view of each patient. With better patient identity management, providers can protect against errors and fraud, and reassure patients that their personal information is safe.

Find out more about Experian Health’s identity management solutions.

Related Posts

Andy’s New WP Workflow Test Article Using Quick Edit

Key takeaways: Revenue cycle teams can use automation to reduce repetitive work and apply AI where data-driven prediction, matching or prioritization can improve a workflow. Experian Health’s 2025 State of Claims survey found that 41% of providers now face denial rates of 10% or higher, while 68% say submitting clean claims is more challenging than a year ago. Patient Access Curator™ (PAC) uses AI to support front-end data validation and insurance discovery, while AI Advantage™ helps teams predict denial risk and prioritize denial follow-up. Artificial intelligence (AI) and automation can support administrative work in healthcare. In the revenue cycle, teams depend on accurate information, timely decisions and efficient follow-up to keep claims moving. In revenue cycle management, AI and automation can help organizations reduce manual checks, find data gaps, predict denial risk and prioritize work queues. These tools are most useful when they support staff judgment, payer expertise and compliance oversight. They can handle repetitive, data-heavy tasks so staff can focus on exceptions and decisions that need human review. In 2023, McKinsey & Company reported that research suggests effectively deploying automation and analytics could eliminate $200 billion to $360 billion of spending in U.S. healthcare. For revenue cycle leaders, the practical question is where to apply those capabilities first. The case for applying AI and automation in healthcare Revenue cycle teams juggle many daily tasks. Staff collect and verify patient information, confirm eligibility, identify the right payer, submit clean claims, monitor status, work denials and manage collections. Small data gaps at the beginning of the process can create downstream rework and delays. Rework also consumes staff time, adding to these operational pressures. As costs rise and revenue cycles tighten, there is increasing pressure to do more with less. Experian Health’s 2025 State of Claims survey found that 54% of providers say claim errors are increasing and 90% of claim denials are reworked with at least some human review before resubmission. Providers are also managing broader financial and administrative pressures. The American Hospital Association has reported that prior authorization requirements, claim audits, denials and other payer policies add administrative burden and cost for hospitals and health systems. These requirements also consume staff time to appeal denials and manage payer processes. AI and automation are different but complementary. Automation follows defined rules to complete repeatable work. AI models can identify patterns in data, predict risk and help teams decide where to focus attention. When used together, they can support more consistent revenue cycle workflows. How AI and automation can support revenue cycle workflows Revenue cycle management automation and AI are most useful when tied to a specific workflow and a measurable operational problem. The goal is to help teams act earlier, reduce avoidable rework and focus staff time where judgment is needed most. For example, automation can complete rule-based eligibility checks. AI can help identify claims with a higher likelihood of denial. In insurance discovery workflows, AI can also help identify coverage that wasn’t captured at registration. When these tools fit into existing workflows, they can support more consistent decisions and reduce manual work. Three practical applications include: 1. Improving front-end data quality with Patient Access Curator Patient and coverage information collected early in the revenue cycle can affect downstream claim outcomes. Incomplete or outdated demographic details, eligibility responses, coordination of benefits or Medicare Beneficiary Identifier information can create problems that lead to claim delays or denials later in the cycle. Experian Health’s Patient Access Curator helps prevent claim denials by validating demographics, eligibility, insurance discovery, coordination of benefits and Medicare Beneficiary Identifier data in seconds. PAC’s AI and machine learning capabilities help improve match accuracy, coverage sequencing and data confidence by writing the validated data back into the host system and sequencing payers before the claim is created. This automates work that would otherwise require manual coverage checks. 2. Using insurance discovery to find coverage not captured at registration When active coverage isn’t identified during registration, claims may be delayed or submitted with incomplete insurance information. Insurance discovery looks for coverage that may not have been captured during registration. Patient Access Curator includes insurance discovery as part of its front-end validation workflow. It can help identify and correct missing or incorrect insurance information so claims can be submitted with more complete coverage data. 3. Using AI to prevent and prioritize denials Even with strong front-end processes, some claims still require additional attention. AI can help claims teams decide which claims to review before submission and which denials to work first after payer response. Experian Health’s AI Advantage supports two denial management use cases:1. AI Advantage – Predictive Denials uses a client’s historical claims data and Experian’s knowledge of payer rules to identify claims with a high likelihood of denial before submission so teams can take corrective action.2. AI Advantage – Denial Triage uses AI to segment denials and identify those with the highest potential for reimbursement. This approach can help teams prioritize with more confidence. Rather than treating every claim or denial the same way, teams can use predictive models to focus on the work that needs the most attention. Potential benefits of AI and automation in the revenue cycle A high-performing revenue cycle depends on timely, accurate and consistent work. AI and automation can help providers modernize that work without losing the expertise of the people who manage complex payer and patient situations every day. When applied to the right workflows, these tools can help organizations: Reduce manual data searches that take staff away from higher-value work Improve front-end data quality before claims are created Identify missing or incorrect coverage information earlier Spot claims that may be at higher risk of denial Prioritize denied claims by potential reimbursement Reduce rework caused by inaccurate or incomplete information Give staff more consistent information for follow-up decisions A focused AI strategy starts with the workflow problem, uses data that is relevant to that problem and keeps staff in control of judgment-based decisions. A more proactive approach to revenue cycle management Revenue cycle teams can move from reactive work toward a more proactive approach: catch errors earlier, validate coverage before claims are created and prioritize the claims and denials that need the most attention. Experian Health offers revenue cycle solutions that use AI and automation in targeted ways to support front-end data quality, reduce rework and manage denials. Patient Access Curator supports registration and coverage validation, while AI Advantage supports denial prediction and triage. Learn more about Experian Health’s Patient Access Curator and AI Advantage.

October 2, 2026 by Andy.Monte@experian.com
Experian Health ranked #1 in Best in KLAS for 2025

Experian Health is very pleased to announce that we've ranked #1 in the 2025 Best in KLAS: Software & Services report, for our Contract Manager and Contract Analysis product, for the third consecutive year. Contract Manager, when paired with Contract Analysis, empowers healthcare providers by ensuring payers comply with contract terms, identifying and recovering underpayments, and arming them with real claims data to negotiate contracts. This enables providers to negotiate more favorable terms and maintain financial stability.  Clarissa Riggins, Chief Product Officer at Experian Health, says, “In the ever-evolving healthcare landscape, our Contract Manager solution has once again been recognized as the #1 Revenue Cycle Management tool by KLAS for the third consecutive year. This prestigious ranking underscores the significant value our solution delivers to our clients by identifying underpayments and facilitating revenue recovery. We are honored to continue supporting our clients with innovative solutions that drive financial success and operational efficiency.”  Learn more about how Contract Manager and Contract Analysis can help your healthcare organization validate reimbursement accuracy, recover underpayments and boost revenue.   Learn more Contact us

February 5, 2025 by kelly.nguyen
How to increase patient engagement

Learn how providers can increase patient engagement, why it matters and key strategies that deliver improved end-to-end patient experiences.

January 30, 2025 by Experian Health

Spotlight test

Spotlight Description

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Sticky Subscribe Title

Sticky Subscribe Description
Sticky Subscribe

Testing Spotlight Paragraph block

Testing the spotlight block header

Archive Testing

Categories